{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "login success!\n",
      "logout success!\n",
      "止损时间:25.04.07 and 止损价格:22.52\n",
      "止损时间:25.04.08 and 止损价格:22.95\n",
      "止损时间:25.04.09 and 止损价格:23.0\n",
      "止损时间:25.04.10 and 止损价格:23.3\n",
      "止损时间:25.04.11 and 止损价格:23.33\n",
      "止损时间:25.04.14 and 止损价格:23.59\n",
      "止损时间:25.04.15 and 止损价格:23.25\n",
      "止损时间:25.04.16 and 止损价格:23.34\n",
      "止损时间:25.04.17 and 止损价格:23.29\n",
      "止损时间:25.04.18 and 止损价格:23.2\n",
      "止损时间:25.04.21 and 止损价格:23.25\n",
      "止损时间:25.04.22 and 止损价格:23.27\n",
      "止损时间:25.04.23 and 止损价格:23.28\n",
      "止损时间:25.04.24 and 止损价格:23.06\n",
      "止损时间:25.04.25 and 止损价格:23.16\n",
      "止损时间:25.04.28 and 止损价格:22.91\n",
      "止损时间:25.04.29 and 止损价格:22.49\n",
      "止损时间:25.04.30 and 止损价格:22.58\n",
      "止损时间:25.05.06 and 止损价格:23.03\n",
      "止损时间:25.05.07 and 止损价格:23.28\n",
      "止损时间:25.05.08 and 止损价格:23.39\n",
      "止损时间:25.05.09 and 止损价格:23.28\n",
      "止损时间:25.05.12 and 止损价格:23.51\n",
      "止损时间:25.05.13 and 止损价格:23.5\n",
      "止损时间:25.05.14 and 止损价格:23.66\n",
      "止损时间:25.05.15 and 止损价格:23.56\n",
      "止损时间:25.05.16 and 止损价格:23.62\n",
      "止损时间:25.05.19 and 止损价格:23.28\n",
      "止损时间:25.05.20 and 止损价格:23.45\n",
      "止损时间:25.05.21 and 止损价格:23.4\n",
      "止损时间:25.05.22 and 止损价格:23.35\n",
      "止损时间:25.05.23 and 止损价格:23.42\n",
      "止损时间:25.05.26 and 止损价格:22.79\n",
      "止损时间:25.05.27 and 止损价格:22.71\n",
      "止损时间:25.05.28 and 止损价格:22.92\n",
      "止损时间:25.05.29 and 止损价格:22.89\n",
      "止损时间:25.05.30 and 止损价格:22.53\n",
      "止损时间:25.06.03 and 止损价格:22.46\n",
      "止损时间:25.06.04 and 止损价格:22.53\n",
      "止损时间:25.06.05 and 止损价格:22.55\n",
      "止损时间:25.06.06 and 止损价格:22.47\n",
      "止损时间:25.06.09 and 止损价格:22.59\n",
      "止损时间:25.06.10 and 止损价格:22.21\n",
      "止损时间:25.06.11 and 止损价格:22.27\n",
      "止损时间:25.06.12 and 止损价格:22.12\n",
      "止损时间:25.06.13 and 止损价格:22.0\n",
      "止损时间:25.06.16 and 止损价格:21.46\n",
      "止损时间:25.06.17 and 止损价格:21.45\n",
      "止损时间:25.06.18 and 止损价格:21.26\n",
      "止损时间:25.06.19 and 止损价格:21.11\n",
      "止损时间:25.06.20 and 止损价格:20.95\n",
      "止损时间:25.06.23 and 止损价格:21.03\n",
      "止损时间:25.06.24 and 止损价格:21.36\n",
      "止损时间:25.06.25 and 止损价格:21.62\n",
      "止损时间:25.06.26 and 止损价格:21.33\n",
      "止损时间:25.06.27 and 止损价格:21.33\n",
      "止损时间:25.06.30 and 止损价格:21.48\n",
      "止损时间:25.07.01 and 止损价格:21.43\n",
      "止损时间:25.07.02 and 止损价格:21.82\n",
      "止损时间:25.07.03 and 止损价格:21.81\n",
      "止损时间:25.07.04 and 止损价格:21.83\n",
      "止损时间:25.07.07 and 止损价格:21.99\n",
      "止损时间:25.07.08 and 止损价格:21.96\n",
      "止损时间:25.07.09 and 止损价格:21.88\n",
      "止损时间:25.07.10 and 止损价格:21.81\n",
      "止损时间:25.07.11 and 止损价格:21.67\n",
      "止损时间:25.07.14 and 止损价格:21.66\n",
      "止损时间:25.07.15 and 止损价格:22.2\n",
      "止损时间:25.07.16 and 止损价格:21.62\n",
      "止损时间:25.07.17 and 止损价格:21.87\n",
      "止损时间:25.07.18 and 止损价格:21.93\n",
      "止损时间:25.07.21 and 止损价格:22.44\n",
      "止损时间:25.07.22 and 止损价格:22.32\n",
      "止损时间:25.07.23 and 止损价格:22.37\n",
      "止损时间:25.07.24 and 止损价格:22.59\n",
      "止损时间:25.07.25 and 止损价格:22.71\n",
      "止损时间:25.07.28 and 止损价格:22.47\n",
      "止损时间:25.07.29 and 止损价格:22.28\n",
      "止损时间:25.07.30 and 止损价格:22.12\n",
      "止损时间:25.07.31 and 止损价格:21.63\n",
      "止损时间:25.08.01 and 止损价格:21.7\n",
      "止损时间:25.08.04 and 止损价格:22.22\n",
      "止损时间:25.08.05 and 止损价格:22.26\n",
      "止损时间:25.08.06 and 止损价格:22.61\n",
      "止损时间:25.08.07 and 止损价格:22.6\n",
      "止损时间:25.08.08 and 止损价格:22.35\n",
      "止损时间:25.08.11 and 止损价格:22.52\n",
      "止损时间:25.08.12 and 止损价格:22.93\n",
      "止损时间:25.08.13 and 止损价格:23.24\n",
      "止损时间:25.08.14 and 止损价格:22.95\n",
      "止损时间:25.08.15 and 止损价格:23.45\n",
      "止损时间:25.08.18 and 止损价格:25.0\n",
      "止损时间:25.08.19 and 止损价格:24.44\n",
      "止损时间:25.08.20 and 止损价格:25.14\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import baostock as bs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import matplotlib.gridspec as gridspec\n",
    "import talib\n",
    "class DefTypesPool():\n",
    "    def __init__(self):\n",
    "        self.routes = {}\n",
    "    # 注册绘图函数\n",
    "    def route_types(self, types_str):\n",
    "        def decorator(f):\n",
    "            self.routes[types_str] = f\n",
    "            return f\n",
    "        return decorator\n",
    "    # 发现绘图函数\n",
    "    def route_output(self, path):\n",
    "        function_val = self.routes.get(path)\n",
    "        if function_val:\n",
    "            return function_val\n",
    "        else:\n",
    "            raise ValueError('Route \"{}\"\" has not been registered'.format(path))\n",
    "        \n",
    "class MplTypesDraw():\n",
    "    mpl = DefTypesPool() \n",
    "\n",
    "    @mpl.route_types(u\"line\")\n",
    "    def line_plot(df_index, df_dat, graph):\n",
    "        # 绘制Line图\n",
    "        for key, val in df_dat.items():\n",
    "            graph.plot(np.arange(0,len(val)), val, label=key, lw=1.0)\n",
    "\n",
    "    \n",
    "    @mpl.route_types(u\"bar\")\n",
    "    def bar_plot(df_index, df_dat, graph):\n",
    "        # 绘制bar图-Volume\n",
    "        graph.bar(np.arange(0, len(df_index)), df_dat['bar_red'], facecolor='red')\n",
    "        graph.bar(np.arange(0, len(df_index)), df_dat['bar_green'], facecolor='green')\n",
    "\n",
    "    @mpl.route_types(u\"annotate\")\n",
    "    def line_plot(df_index, df_dat, graph):\n",
    "        #绘制annotate图\n",
    "        for key, val in df_dat.items():\n",
    "            for kl_index, today in val['andata'].iterrows():\n",
    "                x_posit = df_index.get_loc(kl_index)\n",
    "                graph.annotate(u\"{}\\n{}\".format(key, today.name.strftime(\"%m.%d\")),\n",
    "                               xy=(x_posit, today[val['xy_y']]),\n",
    "                               xycoords='data',\n",
    "                               xytext=(val['xytext'][0], val['xytext'][1]),\n",
    "                               va = val['va'], #点在标注下方\n",
    "                               textcoords='offset points',\n",
    "                               fontsize=val['fontsize'],\n",
    "                               arrowprops=val['arrow'])\n",
    "                \n",
    "    @mpl.route_types(u\"hline\")\n",
    "    def hline_plot(df_index, df_dat, graph):\n",
    "        #绘制hline图\n",
    "        for key, val in df_dat.items():\n",
    "            graph.axhline(val['pos'], c=val['c'],label=key)\n",
    "\n",
    "    @mpl.route_types(u\"filltrade\")\n",
    "    def filltrade_plot(df_index, df_dat, graph):\n",
    "        # 绘制filltrade 图\n",
    "        signal_shift = df_dat['signal'].shift(1)\n",
    "        # 最前面的NAN用-1填充\n",
    "        signal_shift.fillna(value=-1, inplace=True)\n",
    "        list_signal = np.sign(df_dat['signal'] - signal_shift)\n",
    "        bs_signal = list_signal[list_signal != 0]\n",
    "\n",
    "        skip_days = False\n",
    "        for k1_index, value in bs_signal.items():\n",
    "            if (value == 1) and (skip_days == False):\n",
    "                start = df_index.get_loc(k1_index)\n",
    "                skip_days = True\n",
    "            elif (value == -1) and (skip_days == True):\n",
    "                end = df_index.get_loc(k1_index)\n",
    "                skip_days = False\n",
    "\n",
    "                if df_dat['jdval'].iloc[end-1] < df_dat['jdval'].iloc[start]: #赔钱显示绿色\n",
    "                    graph.fill_between(np.arange(start, end), 0, df_dat['jdval'][start:end],color='green', alpha=0.38)\n",
    "                    is_win = False\n",
    "                else: #赚钱显示红色\n",
    "                    graph.fill_between(np.arange(start, end), 0, df_dat['jdval'][start:end],color='red',alpha=0.38)\n",
    "                    is_win = True\n",
    "                graph.annotate('获利\\n' if is_win else '亏损\\n',\n",
    "                               xy = (end, df_dat['jdval'].asof(k1_index)),\n",
    "                               xytext = (df_dat['xytext'][0], df_dat['xytext'][1]),\n",
    "                               xycoords='data',\n",
    "                               va = df_dat['va'],#点在标注下方\n",
    "                               textcoords = 'offset points',\n",
    "                               fontsize=df_dat['fontsize'],\n",
    "                               arrowprops=df_dat['arrow']\n",
    "                               )\n",
    "                # 整个时间序列填充为底色blue透明度alpha小于后标注区间颜色\n",
    "                graph.fill_between(np.arange(0, len(df_index)), 0, df_dat['jdval'], color='blue',alpha=.08)\n",
    "\n",
    "class MultiTraceIf(MplTypesDraw):\n",
    "    app = DefTypesPool()\n",
    "    ###########################\n",
    "    @app.route_types(u\"cash_profit\") #cash profit and retracement\n",
    "    def cash_profit_graph(stock_dat, sub_graph, cash_hold = 100000, slippage = 0.01,c_rate = 5.0 / 10000, t_rate = 1.0 / 1000):\n",
    "        posit_num = 0 #持股数目\n",
    "        skip_days = False # 持股/持币状态\n",
    "\n",
    "        # 最大风险回撤-资金最大回撤\n",
    "        # 绝对收益-资金的度量\n",
    "        for k1_index, today in stock_dat.iterrows():\n",
    "            # 买入/卖出执行代码\n",
    "            if today.Signal == 1 and skip_days == False: # 买入\n",
    "                skip_days = True\n",
    "                posit_num = int(cash_hold / (today.Close + slippage)) #将资金转化为股票\n",
    "                posit_num = int(posit_num / 100) * 100 # 买入股票至少100股，对posit_num向下取整百\n",
    "\n",
    "                buy_cash = posit_num * (today.Close + slippage) # 计算买入股票所需现金\n",
    "                # 计算手续费，不足5元按5元收，并保留2位小数\n",
    "                commission = round(max(buy_cash * c_rate,5),2)\n",
    "                cash_hold = cash_hold - buy_cash - commission\n",
    "            elif today.Signal == -1 and skip_days == True: #卖出 避免未买先卖\n",
    "                skip_days = False\n",
    "                sell_cash = posit_num * (today.Close - slippage) # 计算卖出股票所得现金\n",
    "                # 计算手续费,不足5元按5元收，并保留2位小数\n",
    "                commission = round(max(sell_cash * c_rate,5),2)\n",
    "                # 计算印花税，保留2位小数\n",
    "                tax = round(sell_cash * t_rate, 2)\n",
    "                cash_hold = cash_hold + sell_cash - commission - tax #剩余现金\n",
    "            if skip_days == True: #持股\n",
    "                stock_dat.loc[k1_index, 'total'] = posit_num * today.Close + cash_hold\n",
    "            else: #空仓\n",
    "                stock_dat.loc[k1_index, 'total'] = cash_hold\n",
    "            \n",
    "            # expanding()计算资金曲线当前的滚动最高值\n",
    "            stock_dat['max_total'] = stock_dat['total'].expanding().max()\n",
    "            # 计算资金曲线在滚动最高值之后所回撤的百分比\n",
    "            stock_dat['per_total'] = stock_dat['total'] / stock_dat['max_total']\n",
    "\n",
    "        min_point_df = stock_dat.sort_values(by = ['per_total'])[0:1]\n",
    "        min_point_total = min_point_df.per_total\n",
    "        max_point_df = stock_dat[stock_dat.index <= min_point_total.index[0]].sort_values(by = ['total'], ascending= False)[0:1]\n",
    "        max_point_total = max_point_df.total\n",
    "\n",
    "        print(\"资金最大回撤%5.2f%% 从%s开始至%s结束\" % ((1 - min_point_total.values[0]) * 100, max_point_total.index[0], min_point_total.index[0]))\n",
    "        \n",
    "        line_total = \"资金总体收益 %d; 上涨幅度 %2.f%%\" % (stock_dat['total'].iloc[-1], (stock_dat['total'].iloc[-1] - 100000) / 100000 * 100)\n",
    "        print(line_total)\n",
    "        max_total = \"资金滚动最高值\"\n",
    "\n",
    "        type_dict = {line_total : stock_dat.total, max_total: stock_dat.max_total,}\n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"line\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "        type_dict = {u'资金最大回撤\\n{}'.format(1 - min_point_total.values[0]):\n",
    "                     {\n",
    "                         'andata':min_point_df,\n",
    "                         'va':'top',\n",
    "                         'xy_y': 'total',\n",
    "                         'xytext': (0, stock_dat['High'].mean()),\n",
    "                         'fontsize': 8,\n",
    "                         'arrow': dict(facecolor='green', shrink=0.1)\n",
    "                     },}\n",
    "        \n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"annotate\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "    \n",
    "    @app.route_types(u\"cmp_profit\")\n",
    "    def cmp_profit_graph(stock_dat, sub_graph, para_dat):\n",
    "        #相对收益-策略 vs 基准\n",
    "        stock_dat['benchmark_profit_log'] = np.log(stock_dat.Close / stock_dat.Close.shift(1))\n",
    "        stock_dat.loc[stock_dat.Signal == -1,'Signal'] = 0\n",
    "        stock_dat['trend_profit_log'] = stock_dat['Signal'] * stock_dat.benchmark_profit_log\n",
    "        line_trend_key = \"策略收益%.2f\" % stock_dat['trend_profit_log'].cumsum().iloc[-1]\n",
    "        line_bench_key = \"基准收益%.2f\" % stock_dat['benchmark_profit_log'].cumsum().iloc[-1]\n",
    "        print(\"资金相对收益: %s vs %s\" % (line_trend_key, line_bench_key)) \n",
    "        type_dict = {line_bench_key: stock_dat['benchmark_profit_log'].cumsum(), line_trend_key: stock_dat['trend_profit_log'].cumsum()}\n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"line\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "    \n",
    "    @app.route_types(u\"close_retrace\")\n",
    "    def close_retrace_graph(stock_dat, sub_graph, para_dat):\n",
    "        #度量策略最大风险回撤--收盘价最大回撤\n",
    "        #计算收盘价曲线当前的滚动最高值\n",
    "        stock_dat['max_close'] = stock_dat['Close'].expanding().max()\n",
    "        #计算收盘价曲线再滚动最高值之后所回撤的百分比\n",
    "        stock_dat['per_close'] = stock_dat['Close'] / stock_dat['max_close']\n",
    "\n",
    "        #找出收盘价的最大回撤率交易日\n",
    "        min_point_df = stock_dat.sort_values(by = ['per_close'])[0:1]\n",
    "        min_point_close = min_point_df.per_close\n",
    "        #找出收盘价最高值交易日，并打印显示\n",
    "        max_point_df = stock_dat[stock_dat.index <= min_point_close.index[0]].sort_values(by = ['Close'],ascending=False)[0:1]\n",
    "        max_point_close = max_point_df.Close\n",
    "        \n",
    "        #打印收盘价的最大回撤率与所对应的最高值交易日和最大回撤交易日\n",
    "        print(u\"股价最大回撤%5.2f%% 从%s开始至%s结束\" % ((1 - min_point_close.values[0]) * 100, max_point_close.index[0], min_point_close.index[0]))\n",
    "\n",
    "        type_dict = {'最大收盘价': stock_dat.max_close, \"收盘价\": stock_dat.Close}\n",
    "        \n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"line\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "\n",
    "        type_dict = {\n",
    "            u\"股价最大回撤\\n{}\".format(1 - min_point_close.values):\n",
    "                {\n",
    "                    'andata': min_point_df,\n",
    "                    'va':'top',\n",
    "                    'xy_y': 'Close',\n",
    "                    'xytext':(0, stock_dat['High'].mean()),\n",
    "                    'fontsize': 8,\n",
    "                    'arrow':dict(facecolor='green', shrink =  0.1)\n",
    "                },\n",
    "        }\n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"annotate\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "\n",
    "    @app.route_types(u\"trade\")\n",
    "    def trade_graph(stock_dat, sub_graph, para_dat):\n",
    "        # 交易获利/亏损区间可视化\n",
    "        type_dict = {\n",
    "            'signal': stock_dat.Signal,\n",
    "            'jdval': stock_dat.Close,\n",
    "            'va':'top',\n",
    "            'xy_y':'Close',\n",
    "            'xytext':(0, stock_dat['High'].mean()),\n",
    "            'fontsize': 0,\n",
    "            'arrow': dict(facecolor='yellow',shrink=0.1)\n",
    "        }\n",
    "        view_function = MplTypesDraw.mpl.route_output(u\"filltrade\")\n",
    "        view_function(stock_dat.index, type_dict, sub_graph)\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        MplTypesDraw.__init__(self)\n",
    "        # 创建fig对象\n",
    "        self.fig = plt.figure(figsize=(kwargs['figsize']), dpi=100, facecolor=\"white\")\n",
    "        plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "        plt.rcParams['axes.unicode_minus'] = False \n",
    "        self.graph_dict = {}\n",
    "        self.graph_curr = {}\n",
    "        try:\n",
    "            gs = gridspec.GridSpec(kwargs['nrows'], kwargs['ncols'],left = kwargs['left'],bottom=kwargs['bottom'],right=kwargs['right'],top=kwargs['top'],wspace = kwargs['wspace'],\n",
    "                                   hspace=kwargs['hspace'],height_ratios=kwargs['height_ratios'])\n",
    "        except:\n",
    "            raise Exception(\"para error\")\n",
    "        else:\n",
    "            for i in range(0, kwargs['nrows'], 1):\n",
    "                self.graph_dict[kwargs['subplots'][i]] = self.fig.add_subplot(gs[i,:])\n",
    "    \n",
    "    def log_trade_info(self, stock_dat):\n",
    "        signal_shift = stock_dat.Signal.shift(1)\n",
    "        signal_shift.fillna(value=-1, inplace=True) #序列最前面的NaN值用-1填充\n",
    "        list_signal = np.sign(stock_dat.Signal - signal_shift)\n",
    "        \n",
    "        buy_signal = stock_dat[list_signal.isin([1])]\n",
    "        sell_signal = stock_dat[list_signal.isin([-1])]\n",
    "        if len(buy_signal) > len(sell_signal):\n",
    "            buy_signal = buy_signal.drop(buy_signal.index[-1])\n",
    "\n",
    "        trade_info = pd.DataFrame({'BuyTime':buy_signal.index.strftime(\"%y.%m.%d\"),\n",
    "                                  'SellTime':sell_signal.index.strftime(\"%y.%m.%d\"),\n",
    "                                  'BuyPrice':buy_signal.Close.values,\n",
    "                                  'SellPrice':sell_signal.Close.values})\n",
    "        \n",
    "        trade_info['DiffPrice'] = trade_info.SellPrice - trade_info.BuyPrice\n",
    "        trade_info['PctProfit'] = np.round(trade_info.DiffPrice / trade_info.BuyPrice * 100,2)\n",
    "\n",
    "        win_count = (trade_info.DiffPrice >= 0).sum()\n",
    "        loss_count = (trade_info.DiffPrice <0).sum()\n",
    "        win_profit = trade_info[trade_info.PctProfit >= 0].PctProfit.sum()\n",
    "        loss_profit = trade_info[trade_info.PctProfit < 0].PctProfit.sum()\n",
    "\n",
    "        print(trade_info)\n",
    "        print(f'亏损次数:{loss_count}, 盈利次数:{win_count}, 胜率:{round(win_count / (win_count +loss_count) * 100,2)}%')\n",
    "        if loss_count == 0:\n",
    "            loss_count = 1\n",
    "        print(f'平均亏损:{round((loss_profit / loss_count),2)} % 平均盈利:{round((win_profit / win_count),2)}%')\n",
    "    \n",
    "    def graph_run(self, stock_data, **kwargs):\n",
    "        #绘制子图\n",
    "        self.df_ohlc = stock_data\n",
    "        # 临时把标准输出出重定向到一个文件，然后再反复正常\n",
    "        with open('logtrade.txt', 'w',encoding='utf-8') as f:\n",
    "            oldstdout = sys.stdout\n",
    "            sys.stdout = f\n",
    "            try:\n",
    "                self.log_trade_info(self.df_ohlc)\n",
    "                for key in kwargs:\n",
    "                    self.graph_curr = self.graph_dict[kwargs[key]['graph_name']]\n",
    "                    for path, val in kwargs[key]['graph_type'].items():\n",
    "                        view_function = MultiTraceIf.app.route_output(path)\n",
    "                        view_function(self.df_ohlc, self.graph_curr, val)\n",
    "                    self.graph_attr(**kwargs[key])\n",
    "                plt.show()\n",
    "            finally:\n",
    "                sys.stdout = oldstdout\n",
    "    \n",
    "    def graph_attr(self, **kwargs):\n",
    "        if 'title' in kwargs.keys():\n",
    "            self.graph_curr.set_title(kwargs['title'])\n",
    "        \n",
    "        if 'legend' in kwargs.keys():\n",
    "            self.graph_curr.legend(loc=kwargs['legend'], shadow=True)\n",
    "        \n",
    "        if 'xlabel' in kwargs.keys():\n",
    "            self.graph_curr.set_xlabel(kwargs['xlabel'])\n",
    "\n",
    "        self.graph_curr.set_ylabel(kwargs['ylabel'])\n",
    "        self.graph_curr.set_xlim(0, len(self.df_ohlc.index)) # 设置一下x轴的范围\n",
    "        self.graph_curr.set_xticks(range(0, len(self.df_ohlc.index), kwargs['xticks'])) # x轴刻度设定 每15天标一个日期\n",
    "\n",
    "        if 'xticklabels' in kwargs.keys():\n",
    "            self.graph_curr.set_xticklabels(\n",
    "                [self.df_ohlc.index.strftime(kwargs['xticklabels'])[index] for index in\n",
    "                 self.graph_curr.get_xticks()]) # 标签设置为日期\n",
    "            \n",
    "            # x轴每个ticker标签都向右45度\n",
    "            for label in self.graph_curr.xaxis.get_ticklabels():\n",
    "                label.set_rotation(45)\n",
    "                label.set_fontsize(10) #设置标签字体\n",
    "        else:\n",
    "            for label in self.graph_curr.xaxis.get_ticklabels():\n",
    "                label.set_visible(False)\n",
    "            \n",
    "\n",
    "\n",
    "# 登陆系统\n",
    "def bs_k_data_stock(code_val='sz.000651',start_val = '2025-01-01', end_val='2025-05-01',freq_val='d',adjust_val='3'):\n",
    "    lg = bs.login()\n",
    "    # 获取历史行情数据\n",
    "    fields = \"date,open,high,low,close,volume\"\n",
    "    df_bs = bs.query_history_k_data_plus(code_val, fields, start_date=start_val, end_date=end_val,frequency=freq_val ,adjustflag=adjust_val)\n",
    "    #日K线，3，默认不赋权，1 后复权，2 前复权\n",
    "    data_list= []\n",
    "    while (df_bs.error_code == '0') & df_bs.next():\n",
    "        #获取一条记录，将记录合并在一起\n",
    "        data_list.append(df_bs.get_row_data())\n",
    "    result = pd.DataFrame(data_list, columns=df_bs.fields)\n",
    "\n",
    "    result.close = result.close.astype('float64')\n",
    "    result.open = result.open.astype('float64')\n",
    "    result.low = result.low.astype('float64')\n",
    "    result.high = result.high.astype('float64')\n",
    "    result.volume = result.volume.astype('float64')\n",
    "    result.volume = result.volume/100 #单位转换:股-手\n",
    "    result.date = pd.DatetimeIndex(result.date)\n",
    "    result.set_index(\"date\", drop=True, inplace= True)\n",
    "    result.index = result.index.set_names('Date')\n",
    "\n",
    "    recon_data = {'High' : result.high, 'Low': result.low, 'Open': result.open, 'Close':result.close, 'Volume': result.volume}\n",
    "    df_recon = pd.DataFrame(recon_data)\n",
    "\n",
    "    #退出系统\n",
    "    bs.logout()\n",
    "    return df_recon\n",
    "\n",
    "\n",
    "def get_trade_signal(stock_dat):\n",
    "    df_csvload_trade = pd.read_csv('600681.csv',index_col=None,parse_dates=[2,3])\n",
    "    stock_dat = stock_dat.assign(Signal = np.nan)\n",
    "    stock_dat.loc[df_csvload_trade[\"Buy-Time\"],'Signal'] = 1\n",
    "    stock_dat.loc[df_csvload_trade[\"Sell-Time\"],'Signal'] = -1\n",
    "    # 与前面元素保持一致\n",
    "    stock_dat.ffill(inplace=True)\n",
    "    # 序列最前面几个NaN用-1填充\n",
    "    stock_dat.fillna(value = -1, inplace = True)\n",
    "    return stock_dat\n",
    "\n",
    "def get_ndays_signal(stock_dat, N1= 15, N2 = 5):\n",
    "    # 海龟策略，唐奇安通道突破（N日突破）买入/卖出信号\n",
    "    stock_dat['N1_High'] = stock_dat.High.rolling(window=N1).max() #计算最近N1个交易日最高价\n",
    "    expan_max = stock_dat.High.expanding().max() #滚动计算当前交易日为止的最大值\n",
    "    stock_dat.fillna({'N1_High':expan_max},inplace=True) #填充前N1个nan\n",
    "    stock_dat['N2_Low'] = stock_dat.Low.rolling(window=N2).min() #计算最近N2个交易日最低价\n",
    "    expan_min = stock_dat.Low.expanding().min()\n",
    "    stock_dat.fillna({'N2_Low':expan_min},inplace=True) #目前出现过的最小值填充前N2个nan\n",
    "    # 收盘价超过N1最高价，买入股票\n",
    "    buy_index = stock_dat[stock_dat.Close > stock_dat.N1_High.shift(1)].index\n",
    "    # 收盘价超过N2最高价，卖出股票\n",
    "    sell_index = stock_dat[stock_dat.Close < stock_dat.N2_Low.shift(1)].index\n",
    "    stock_dat.loc[buy_index, 'Signal'] = 1\n",
    "    stock_dat.loc[sell_index,'Signal'] = -1\n",
    "    stock_dat['Signal'] = stock_dat.Signal.shift(1)\n",
    "    stock_dat.ffill( inplace= True) #与前面元素保持一致\n",
    "    stock_dat.fillna({'Signal':-1},inplace = True) #序列最前面几个NaN值用-1填充\n",
    "    return stock_dat\n",
    "\n",
    "\n",
    "def get_ndays_atr_signal(stock_dat, N1 = 15, N2 = 5, n_win = 3.5, n_loss = 1.8):\n",
    "    # 海龟策略-唐奇安通道突破（N日突破）买入/卖出信号\n",
    "    stock_dat['N1_High'] = stock_dat.High.rolling(window=N1).max() #计算最近N1个交易日最高价\n",
    "    expan_max = stock_dat.High.expanding().max() #滚动计算当前交易日为止的最大值\n",
    "    stock_dat.fillna({'N1_High':expan_max}, inplace=True) #填充前N1个nan\n",
    "    stock_dat['N2_Low'] = stock_dat.Low.rolling(window=N2).min() #计算租金N2个交易日最低价\n",
    "    expan_min = stock_dat.Low.expanding().min()\n",
    "    stock_dat.fillna({'N2_Low':expan_min}, inplace=True) #目前出现过的最小值填充前N2个nan\n",
    "\n",
    "    stock_dat['ATR21'] = talib.ATR(stock_dat.High.values, stock_dat.Low.values, stock_dat.Close.values, timeperiod=21)\n",
    "    # 收盘价超过N1最高价 买入股票\n",
    "    buy_index = stock_dat[stock_dat.Close > stock_dat.N1_High.shift(1)].index\n",
    "    # 收盘价超过N2最低价 卖出股票\n",
    "    sell_index = stock_dat[stock_dat.Close < stock_dat.N2_Low.shift(1)].index\n",
    "    stock_dat.loc[buy_index, 'Signal'] = 1\n",
    "    stock_dat.loc[sell_index, 'Signal'] = -1\n",
    "    stock_dat['Signal'] = stock_dat.Signal.shift(1)\n",
    "    buy_price = 0\n",
    "\n",
    "    for k1_index, today in stock_dat.iterrows():\n",
    "        if (buy_price == 0) and (today.Signal == 1):\n",
    "            buy_price = today.Close\n",
    "        # 到达收盘价少于买入价后触发卖出\n",
    "        elif(buy_price != 0) and (buy_price > today.Close) and ((buy_price-today.Close) > n_loss * today.ATR21):\n",
    "            print(f'止损时间:{k1_index.strftime(\"%y.%m.%d\")} and 止损价格:{round(today.Close,2)}')\n",
    "            stock_dat.loc[k1_index, 'Signal'] = -1\n",
    "        # 到达收盘价多余买入价后触发卖出\n",
    "        elif(buy_price  != 0) and (buy_price < today.Close) and ((today.Close - buy_price) > n_win * today.ATR21):\n",
    "            print(f'止盈时间:{k1_index.strftime(\"%y.%m.%d\")} and 止盈价格:{round(today.Close,2)}')\n",
    "            stock_dat.loc[k1_index, 'Signal'] = -1\n",
    "            buy_price = 0 \n",
    "        else:\n",
    "            pass\n",
    "    stock_dat['Signal'] = stock_dat['Signal'].ffill() #与前面元素保持一致\n",
    "    stock_dat.fillna({'Signal':-1}, inplace=True) #序列最前面几个NaN值用0填充\n",
    "    return stock_dat\n",
    "        \n",
    "\n",
    "\n",
    "df_stockload = bs_k_data_stock('sh.601633','2025-01-01', '2025-08-26')\n",
    "layout_dict = {\n",
    "    'figsize':(14, 8),\n",
    "    'nrows': 3,\n",
    "    'ncols': 1,\n",
    "    'left': 0.08,\n",
    "    'bottom':0.15,\n",
    "    'right':0.95,\n",
    "    'top':0.95,\n",
    "    'wspace':None,\n",
    "    'hspace':0,\n",
    "    'height_ratios': [1.5, 1, 1],\n",
    "    'subplots':['kgraph','cashgraph','cmppfgraph']\n",
    "}\n",
    "subplots_dict = {'graph_fst':{'graph_name':'kgraph',\n",
    "                              'graph_type':{'trade':None,\n",
    "                                            'close_retrace':None\n",
    "                                            },\n",
    "                              'title':u\"回测分析\",\n",
    "                              'ylabel':u'价格',\n",
    "                              'xticks':15,\n",
    "                              'legend':'best'\n",
    "                              },\n",
    "                'graph_sec':{'graph_name':'cashgraph',\n",
    "                              'graph_type':{'cash_profit':100000 #初始资金\n",
    "                                            },\n",
    "                              'ylabel':u'资金收益和回撤',\n",
    "                              'xticks':15,\n",
    "                              'legend':'best'\n",
    "                              },\n",
    "                'graph_fth':{'graph_name':'cmppfgraph',\n",
    "                              'graph_type':{'cmp_profit':None\n",
    "                                            },\n",
    "                              'ylabel':u'策略收益vs基准收益',\n",
    "                              'xlabel':u'日期',\n",
    "                              'xticks':15,\n",
    "                              'legend':'best',\n",
    "                              'xticklabel':'%Y-%m-%d'\n",
    "                              },\n",
    "                              }\n",
    "draw_stock = MultiTraceIf(**layout_dict)\n",
    "df_stockload2 = get_ndays_atr_signal(df_stockload)\n",
    "#df_stockload2 = get_trade_signal(df_stockload)\n",
    "#df_stockload2 = get_ndays_signal(df_stockload)\n",
    "draw_stock.graph_run(df_stockload2, **subplots_dict)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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